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from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
import torchvision
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers import ModelMixin
from diffusers.utils import BaseOutput
from diffusers.utils.import_utils import is_xformers_available
from diffusers.models.attention import Attention as CrossAttention, FeedForward
from einops import rearrange, repeat
import math
def zero_module(module):
# Zero out the parameters of a module and return it.
for p in module.parameters():
p.detach().zero_()
return module
@dataclass
class TemporalTransformer3DModelOutput(BaseOutput):
sample: torch.FloatTensor
if is_xformers_available():
import xformers
import xformers.ops
else:
xformers = None
def get_motion_module(
in_channels,
motion_module_type: str,
motion_module_kwargs: dict
):
if motion_module_type == "Vanilla":
return VanillaTemporalModule(in_channels=in_channels, **motion_module_kwargs,)
else:
raise ValueError
class VanillaTemporalModule(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads = 8,
num_transformer_block = 2,
attention_block_types =( "Temporal_Self", "Temporal_Self" ),
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 24,
temporal_attention_dim_div = 1,
zero_initialize = True,
):
super().__init__()
self.temporal_transformer = TemporalTransformer3DModel(
in_channels=in_channels,
num_attention_heads=num_attention_heads,
attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div,
num_layers=num_transformer_block,
attention_block_types=attention_block_types,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
if zero_initialize:
self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out)
def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None):
hidden_states = input_tensor
hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask)
output = hidden_states
return output
class TemporalTransformer3DModel(nn.Module):
def __init__(
self,
in_channels,
num_attention_heads,
attention_head_dim,
num_layers,
attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
dropout = 0.0,
norm_num_groups = 32,
cross_attention_dim = 768,
activation_fn = "geglu",
attention_bias = False,
upcast_attention = False,
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 24,
):
super().__init__()
inner_dim = num_attention_heads * attention_head_dim
self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True)
self.proj_in = nn.Linear(in_channels, inner_dim)
self.transformer_blocks = nn.ModuleList(
[
TemporalTransformerBlock(
dim=inner_dim,
num_attention_heads=num_attention_heads,
attention_head_dim=attention_head_dim,
attention_block_types=attention_block_types,
dropout=dropout,
norm_num_groups=norm_num_groups,
cross_attention_dim=cross_attention_dim,
activation_fn=activation_fn,
attention_bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
for d in range(num_layers)
]
)
self.proj_out = nn.Linear(inner_dim, in_channels)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None):
assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}."
video_length = hidden_states.shape[2]
hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w")
batch, channel, height, weight = hidden_states.shape
residual = hidden_states
hidden_states = self.norm(hidden_states)
inner_dim = hidden_states.shape[1]
hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim)
hidden_states = self.proj_in(hidden_states)
# Transformer Blocks
for block in self.transformer_blocks:
hidden_states = block(hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length)
# output
hidden_states = self.proj_out(hidden_states)
hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous()
output = hidden_states + residual
output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length)
return output
class TemporalTransformerBlock(nn.Module):
def __init__(
self,
dim,
num_attention_heads,
attention_head_dim,
attention_block_types = ( "Temporal_Self", "Temporal_Self", ),
dropout = 0.0,
norm_num_groups = 32,
cross_attention_dim = 768,
activation_fn = "geglu",
attention_bias = False,
upcast_attention = False,
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 24,
):
super().__init__()
attention_blocks = []
norms = []
for block_name in attention_block_types:
attention_blocks.append(
VersatileAttention(
attention_mode=block_name.split("_")[0],
cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None,
query_dim=dim,
heads=num_attention_heads,
dim_head=attention_head_dim,
dropout=dropout,
bias=attention_bias,
upcast_attention=upcast_attention,
cross_frame_attention_mode=cross_frame_attention_mode,
temporal_position_encoding=temporal_position_encoding,
temporal_position_encoding_max_len=temporal_position_encoding_max_len,
)
)
norms.append(nn.LayerNorm(dim))
self.attention_blocks = nn.ModuleList(attention_blocks)
self.norms = nn.ModuleList(norms)
self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn)
self.ff_norm = nn.LayerNorm(dim)
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
for attention_block, norm in zip(self.attention_blocks, self.norms):
norm_hidden_states = norm(hidden_states)
hidden_states = attention_block(
norm_hidden_states,
encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None,
video_length=video_length,
) + hidden_states
hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states
output = hidden_states
return output
class PositionalEncoding(nn.Module):
def __init__(
self,
d_model,
dropout = 0.,
max_len = 24
):
super().__init__()
self.dropout = nn.Dropout(p=dropout)
position = torch.arange(max_len).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
pe = torch.zeros(1, max_len, d_model)
pe[0, :, 0::2] = torch.sin(position * div_term)
pe[0, :, 1::2] = torch.cos(position * div_term)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:, :x.size(1)]
return self.dropout(x)
class VersatileAttention(CrossAttention):
def __init__(
self,
attention_mode = None,
cross_frame_attention_mode = None,
temporal_position_encoding = False,
temporal_position_encoding_max_len = 24,
*args, **kwargs
):
super().__init__(*args, **kwargs)
assert attention_mode == "Temporal"
self.attention_mode = attention_mode
self.is_cross_attention = kwargs["cross_attention_dim"] is not None
self.pos_encoder = PositionalEncoding(
kwargs["query_dim"],
dropout=0.,
max_len=temporal_position_encoding_max_len
) if (temporal_position_encoding and attention_mode == "Temporal") else None
def extra_repr(self):
return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}"
def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None):
batch_size, sequence_length, _ = hidden_states.shape
if self.attention_mode == "Temporal":
d = hidden_states.shape[1]
hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length)
if self.pos_encoder is not None:
hidden_states = self.pos_encoder(hidden_states)
encoder_hidden_states = repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) if encoder_hidden_states is not None else encoder_hidden_states
else:
raise NotImplementedError
encoder_hidden_states = encoder_hidden_states
if self.group_norm is not None:
hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = self.to_q(hidden_states)
dim = query.shape[-1]
query = self.head_to_batch_dim(query)
if self.added_kv_proj_dim is not None:
raise NotImplementedError
encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
key = self.to_k(encoder_hidden_states)
value = self.to_v(encoder_hidden_states)
key = self.head_to_batch_dim(key)
value = self.head_to_batch_dim(value)
if attention_mask is not None:
if attention_mask.shape[-1] != query.shape[1]:
target_length = query.shape[1]
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
attention_mask = attention_mask.repeat_interleave(self.heads, dim=0)
if not hasattr(self, '_use_memory_efficient_attention_xformers'):
self._use_memory_efficient_attention_xformers = True
# attention, what we cannot get enough of
if self._use_memory_efficient_attention_xformers:
self.set_use_memory_efficient_attention_xformers(True)
# hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask)
# Some versions of xformers return output in fp32, cast it back to the dtype of the input
hidden_states = hidden_states.to(query.dtype)
else:
if self._slice_size is None or query.shape[0] // self._slice_size == 1:
hidden_states = self._attention(query, key, value, attention_mask)
else:
hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask)
# linear proj
hidden_states = self.to_out[0](hidden_states)
# dropout
hidden_states = self.to_out[1](hidden_states)
if self.attention_mode == "Temporal":
hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d)
return hidden_states
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